Interior-point method

Algorithms for solving convex optimization problems

Interior-point methods (also referred to as barrier methods or IPMs) are algorithms for solving linear and non-linear convex optimization problems. IPMs combine two advantages of previously-known algorithms: Theoretically, their run-time is polynomial—in contrast to the simplex method, which has exponential run-time in the worst case.

Nº Q461992 ★

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Interior-point method

Algorithms for solving convex optimization problems

Interior-point methods (also referred to as barrier methods or IPMs) are algorithms for solving linear and non-linear convex optimization problems. IPMs combine two advantages of previously-known algorithms: Theoretically, their run-time is polynomial—in contrast to the simplex method, which has exponential run-time in the worst case.

From Wikipedia

Interior-point methods (also referred to as barrier methods or IPMs) are algorithms for solving linear and non-linear convex optimization problems. IPMs combine two advantages of previously-known algorithms: Theoretically, their run-time is polynomial—in contrast to the simplex method, which has exponential run-time in the worst case. Practically, they run as fast as the simplex method—in contrast to the ellipsoid method, which has polynomial run-time in theory but is very slow in practice. In contrast to active-set methods (such as the simplex method) which traverses the boundary of the feasible region, and the ellipsoid method which bounds the feasible region from outside, an IPM reaches a best solution by traversing the interior of the feasible region—hence the name.

Text: Wikipédia, CC BY-SA 4.0. · Image: Gjacquenot (CC BY-SA 4.0) ·

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